Machine learning gets a lot of buzz. The two most talked about classes of algorithms are classification and clustering. Classification is assigning things a label.

A classification problem is a supervised learning problem that asks for a choice between two or more classes, usually providing probabilities for each class. Leaving out neural networks and deep ...

Deep learning is so popular today due to two main reasons. First it was discovered that CNNs run much faster on GPUs, such as NVidia‘s Tesla K80 processor. Secondly, data scientists realized that the ...

What are the differences between econometrics, statistics, and machine learning? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and ...

Cluster analysis, a commonly used machine-learning technique, uses these basic features to not only categorize materials and ...

In machine learning, typically non-linear regression techniques are used. Examples of nonlinear regression algorithms include gradient descent, Gauss-Newton, and the Levenberg-Marquardt methods.

A Tokyo Tech study introduced a machine learning-powered clustering model that incorporates both basic features and target properties, successfully grouping over 1,000 inorganic materials.